There are currently two solutions available for exploiting additional LF information for face expression recognition. Inspired by [1]}, a deep learning spatio-angular fusion framework was adopted in [2]}, to model both the spatial and angular information with VGG-16 and LSTM recurrent networks. In [3]}, the deep framework proposed in [2]} was extended by exploring both forward and backward angular relations using bidirectional LSTM. Additionally, an attention mechanism selectively focuses on the most important spatio-angular features, thus learning the final features more effectively.
